unravelling the environmental drivers of deep-sea …effect of diversity on ecosystem functioning....

Biogeosciences, 10, 3127–3143, 2013www.biogeosciences.net/10/3127/2013/doi:10.5194/bg-10-3127-2013© Author(s) 2013. CC Attribution 3.0 License.

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Unravelling the environmental drivers of deep-sea nematodebiodiversity and its relation with carbon mineralisation along alongitudinal primary productivity gradient

E. Pape1, T. N. Bezerra1, D. O. B. Jones2, and A. Vanreusel1

1Marine Biology Research Group, Krijgslaan 281/S8, 9000 Ghent, Belgium2National Oceanography Centre, European Way, Southampton SO14 3ZH, UK

Correspondence to:E. Pape ([email protected])

Received: 29 November 2012 – Published in Biogeosciences Discuss.: 20 December 2012Revised: 7 April 2013 – Accepted: 19 April 2013 – Published: 8 May 2013

Abstract. Alongside a primary productivity gradient be-tween the Galicia Bank region in the Northeast Atlantic andthe more oligotrophic eastern Mediterranean Basin, we in-vestigated the bathymetric (1200–3000 m) and longitudinalvariation in several measures for nematode taxon (Shannon–Wiener genus diversity, expected genus richness and genericevenness) and functional diversity (trophic diversity, diver-sity of life history strategies, biomass diversity and phylo-genetic diversity). Our goals were to establish the form ofthe relation between diversity and productivity (measured asseafloor particulate organic carbon or POC flux), and to ver-ify the positive and negative effect of sediment particle sizediversity (SED) and the seasonality in POC flux (SVI), re-spectively, on diversity, as observed for other oceanographicregions and taxa. In addition, we hypothesised that highertaxon diversity is associated with higher functional diver-sity, which in turn stimulates nematode carbon mineralisa-tion rates (determined from biomass-dependent respirationestimates). Taxon diversity related positively to seafloor POCflux. Phylogenetic diversity (measured as average taxonomicdistinctness) was affected negatively by the magnitude andvariability in POC flux, and positively by SED. The latteralso showed an inverse relation with trophic diversity. Ac-counting for differences in total biomass between samples,we observed a positive linear relation between taxon diver-sity and carbon mineralisation in nematode communities. Wecould, however, not identify the potential mechanism throughwhich taxon diversity may promote this ecosystem functionsince none of the functional diversity indices related to bothdiversity and nematode respiration. The present results sug-

gest potential effects of climate change on deep-sea ecosys-tem functioning, but further also emphasise the need fora better understanding of nematode functions and their re-sponse to evolutionary processes.

1 Introduction

Biodiversity within deep-sea sediments exhibits clear geo-graphic variation. Potentially simultaneously acting driversof variation in local diversity include productivity, bound-ary constraints, sediment heterogeneity, oxygen availabil-ity, hydrodynamic regimes and catastrophic physical distur-bance (Levin et al., 2001). Gradients in these environmen-tal factors co-determine local diversity by influencing therates of local processes like resource partitioning, competi-tion, predation, physical disturbance, etc. Bathymetric vari-ation in diversity is one of the most studied geographicaldiversity trends (e.g. Danovaro et al., 2008b; Rex and Et-ter, 2010; Tecchio et al., 2011). Benthic diversity gener-ally shows a hump-shaped bathymetric pattern, with a peakaround 1500–2500 m depth (Rex and Etter, 2010; Stuart etal., 2003). However, the unimodal relationship between di-versity and water depth is not universal and the form of theassociation varies between regions (Danovaro et al., 2010;Stuart et al., 2003). The depth-related gradient in diversityis believed to be governed by productivity (i.e. the particu-late organic carbon (POC) flux) and/or sediment character-istics (Gray, 2002; Stuart et al., 2003). Deep-sea diversityhas been documented to vary positively (Glover et al., 2002;

Published by Copernicus Publications on behalf of the European Geosciences Union.

3128 E. Pape et al.: Diversity and function of deep-sea nematodes

Lambshead et al., 2000, 2002), negatively (Gooday et al.,2012) or unimodally (Leduc et al., 2012a; McClain et al.,2012; Tittensor et al., 2011) with productivity for differenttaxa and geographic regions. These differences in diversity–productivity trends may be related to the differential produc-tivity ranges considered (Rex and Etter, 2010). A unimodalcurve may only be found when the range of productivity issufficiently large, whereas a positive and linear relation maybe retrieved under a low- and high-productivity regime, re-spectively. The magnitude of productivity is assumed to havea positive effect on diversity (through the stimulation of pop-ulation growth), whilst temporal variability in productivitymay depress diversity (by limiting feeding to certain peri-ods of the year) (Chown and Gaston, 1999). Seasonality insurface productivity had an adverse effect on foraminiferalspecies diversity in abyssal sediments (Corliss et al., 2009;Gooday et al., 2012). The diversity of sediment particles,which can be regarded as a measure of habitat heterogeneity,has a positive influence on macrofaunal (Etter and Grassle,1992) and nematode (Leduc et al., 2011) species diversity inthe western North Atlantic and in the Pacific Ocean, respec-tively.

As a consequence of the worldwide ongoing decline inmarine and terrestrial biodiversity (Pereira et al., 2010), therehas been an explosion in the number of studies addressingthe effect of biodiversity on the functioning of ecosystems(reviewed by Balvanera et al., 2006; Hooper et al., 2002;Stachowicz et al., 2007). There are four main possible im-pact scenarios of biodiversity on an ecosystem function: (1)no effect (null model); (2) all taxa (species/genera, etc.) con-tribute to ecosystem functioning (rivet hypothesis); (3) thereis a minimum need of species, and all other species are redun-dant (redundancy model); (4) the effect is not predictable (id-iosyncratic model) (Lawton, 1994; Naeem et al., 1995) Ac-cording to different authors, the nature and strength of the re-lation between diversity and an ecosystem function dependson the environmental factors that drive diversity and ecosys-tem processes (Bengtsson et al., 2002; Cardinale et al., 2000)and the ecosystem function considered (Bolam et al., 2002;Hiddink et al., 2009; Naeem et al., 1995).

Numerous biodiversity–ecosystem function studies relatedtaxon diversity (i.e. the diversity of taxa, with taxa indicat-ing species, genera or other taxonomic levels), and primar-ily taxon richness (i.e. the number of taxa), to the rate ofecosystem processes, assuming this diversity measure servesas an adequate surrogate for functional diversity (Naeem andWright, 2003). However, taxa may differ in their contribu-tion to total functional diversity (degree of redundancy andsingularity) and/or total abundance (commonness–rarity), re-sulting in a huge variety in possible relationships betweentaxon and functional diversity (Cadotte et al., 2011; Naeemand Wright, 2003). Moreover, the nature of the relation be-tween taxon and functional diversity depends on the measureof functional diversity employed (Naeem and Wright 2003).Analogous to taxon diversity, different aspects of functional

diversity can be measured – namely richness, divergence andevenness (Mason et al., 2005). Numerous univariate and mul-tivariate indices have been developed that fall into one ofthese categories (Weiher, 2011). Because functional diver-sity provides a direct mechanistic link between diversity andecosystem functioning, a growing amount of research hasbeen devoted to the effect of functional – instead of taxon –diversity on ecosystem functioning (Dıaz and Cabido, 2001;Petchey et al., 2004; Reiss et al., 2009). In many studieswhere both functional and taxon diversity were related tothe rate of ecosystem processes, functional diversity or com-position explained a greater portion of ecosystem function-ing than traditional measures of taxon diversity (Dıaz andCabido, 2001; Petchey et al., 2004).

Contrary to taxon diversity, phylogenetic diversity entailsthe evolutionary relationships amongst taxa (Vellend et al.,2010). When it is difficult to identify or measure those prop-erties that are relevant to the ecosystem function under study,phylogenetic diversity may be a useful proxy for functionaldiversity since it often encompasses most of the variation infunctional traits within a community (Cadotte et al., 2011;Srivastava et al., 2012). The rationale behind this approachis that phylogenetic relatedness usually indicates ecologicalresemblance, i.e. the more closely related two individualsare, the higher the likelihood that they are functionally sim-ilar (but see e.g. Gravel et al., 2012; Srivastava et al., 2012).Cadotte et al. (2008, 2009) discovered that phylogenetic di-versity was a better predictor of ecosystem functioning thanboth species and functional group richness. Moreover, notonly individuals belonging to different species may differ infunctional characteristics, but also considerable intraspecificvariability in functional traits is known to occur (Bolnick etal., 2011; Messier et al., 2010). This finding calls for a trait-based instead of a taxon-based approach in examining theeffect of diversity on ecosystem functioning.

Deep-sea nematodes are highly diverse (Lambshead andBoucher, 2003), and owing to their omnipresence they canbe used to study broad-scale geographic patterns in diver-sity (Lambshead et al., 2002) as well as the importance ofdiversity to ecosystem functioning (Danovaro et al., 2008a).Nematodes may influence an important ecosystem functionlike the bacterial breakdown of organic matter through bio-turbation and irrigation (Pike et al., 2001), thereby enhanc-ing nutrient and/or oxygen fluxes (Alkemade et al., 1992;Aller and Aller, 1992), bacterivory (De Mesel et al., 2003)or the provision of optimal growth conditions for bacte-ria in their mucus trails (Moens et al., 2005; Riemann andHelmke, 2002). Here, we investigated the variation in ne-matode taxon (genus) and functional diversity along longi-tudinal (reaching from the Galicia Bank in the Northeast At-lantic to the eastern Mediterranean Basin) and bathymetric(1200–1900–3000 m) gradients within deep-sea sediments.The first aim of this study was to identify potential environ-mental drivers (i.e. magnitude and variability in seafloor par-ticulate organic carbon (POC) flux and sediment particle size

Biogeosciences, 10, 3127–3143, 2013 www.biogeosciences.net/10/3127/2013/

E. Pape et al.: Diversity and function of deep-sea nematodes 3129

diversity) of nematode taxon and functional diversity. Specif-ically, we explored the form of the diversity–productivitycurve (unimodal, positive or negative) by characterising therelationship between nematode diversity and the magnitudeof the POC flux to the seabed. Since most of our stationswere located within the oligotrophic Mediterranean Sea, weexpected to see a positive relation between diversity andseafloor POC flux. Our second aim was to determine hownematode diversity relates to ecosystem functioning. Con-cretely, we presumed that higher taxon diversity results inhigher functional diversity, which in turn stimulates nema-tode carbon mineralisation. Danovaro et al. (2008a) observedan exponential relationship between nematode species diver-sity and ecosystem functioning, and so we may expect thistype of relation for nematode genus diversity as well if wepresume that higher relatedness results in higher functionalsimilarity. The rate of carbon mineralisation by the nema-tode community was assessed by estimating respiration ratesfrom biomass measurements.

2 Materials and methods

2.1 Study region and sampling strategy

Sediment samples were collected at 1200, 1900 and 3000 mwater depth along a longitudinal transect spanning the Gali-cia Bank in the Northeast Atlantic and the MediterraneanBasin (Fig. 1, Table 1). The regions that were sampled were,from west to east, the Galicia Bank region, and the Alge-rian, Algero-Provencal, Ionian and Levantine basins in theMediterranean Sea. Samples comprised either subsamplesfrom box cores taken with multicorer cores or actual mul-ticorer samples. We used cores with differing surface areas(see 2.3 and Table 1), but standardised subsamples of max-imum 100 nematodes per sediment layer were used for di-versity analysis. Sediment cores were sliced horizontally percm down to 5 cm, and from 5 to 10 cm sediment depth. Next,these sediment sections were fixed in seawater-buffered 4 %formalin.

2.2 Environmental variables

Grain size data were available for the top 5 cm of the sedi-ment, and were averaged over the five sediment depth lay-ers. Sediment particle size diversity (SED) was computedas the Shannon–Wiener diversity index based on the percentdry weight of 10 particle size classes (i.e.< 4, 4–38, 38–63, 63–125, 125–250, 250–500, 500–800, 800–1000, 1000–1600, > 1600 µm) (Etter and Grassle, 1992; Leduc et al.,2011). The seasonal variability in surface primary produc-tivity (SVI) was calculated as the coefficient of variation(i.e. standard deviation divided by the mean) of monthly netprimary productivity (NPP) values (Lutz et al., 2007), whichwere extracted from the vertically generalised productionmodel (VGPM; resolution: 1◦) (Behrenfeld and Falkowski,

1997) and downloaded fromhttp://www.science.oregonstate.edu/ocean.productivity/. We considered SVI as a proxy forthe intermittency with which organic matter is depositedat the deep-sea bed (referred to as seasonality or seasonalvariability in POC flux in the remainder of the text). TheVGPM estimate of NPP values was based on satellite mea-surements of sea surface temperature (SST), surface waterChl a concentrations, and photosynthetically active radiation.Estimates of the particulate organic carbon (POC) flux to theseafloor (abbreviated as POC in the remainder of the text)were approximated on the basis of water depth and SVI val-ues following Lutz et al. (2007).

2.3 Nematode diversity

The formalin-fixed sediment samples were washed over a32 µm mesh sieve, and the meiofauna were extracted from thesediment by Ludox centrifugation (Heip et al., 1985). Wherepossible, around 100 nematodes were hand picked from eachsediment layer and identified to genus level. Genus abun-dance data for the top 0 to 10 cm of each sediment core wereobtained by summing genus counts in all sediment slices,taking into account total nematode abundances in each slice.Diversity indices were calculated per core and hence sig-nify point diversity values. Genus diversity was evaluatedby means of expected genus richness EG(20), Pielou’s even-ness (J ′), as well as Shannon–Wiener diversity (H ′, loge),which incorporates both the number of genera and their rela-tive abundances. Functional nematode diversity was assessedusing the following metrics:

– On the basis of the morphology of the buccal cavity,nematode genera can be appointed to one of the fol-lowing four feeding types: selective deposit feeder (1A),non-selective deposit feeder (1B), epistrate feeder (2A)and predators/scavengers (2B) (Wieser, 1953). Nema-todetrophic diversity(TD) was computed as the recip-rocal of the trophic diversity index given by Heip etal. (1985):

TD =1

4∑i=1

q2i

,

where qi = the relative abundance of feeding typei.Consequently, the value of TD varied between 1 (all in-dividuals belong to the same feeding guild) and 4 (all4 feeding types comprise the same number of individ-uals). Since all four feeding guilds were represented inall sediment cores studied, TD could be considered as ameasure of trophic evenness (Mason et al., 2005).

– Based on their life history strategies, nematode generacan be assigned a c–p (coloniser–persister) score rang-ing between 1 (colonisers: short generation time, highreproduction rate and colonisation ability and tolerant

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http://www.science.oregonstate.edu/ocean.productivity/

http://www.science.oregonstate.edu/ocean.productivity/


Figures 904

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Fig. 1. Map with sampling locations. Colours indicate approximate water depth (white: 1200 m, grey: 907 1900 m, black: 3000 m). The rectangles enclose samples that were collected within the same region (GB: 908 Galicia Bank region, A: Algerian basin, AP: Algero-Provençal basin, I: Ionian basin and L: Levantine basin). 909

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Fig. 1.Map with sampling locations. Colours indicate approximate water depth (white: 1200 m, grey: 1900 m, black: 3000 m). The rectanglesenclose samples that were collected within the same region (GB: Galicia Bank region, A: Algerian Basin, AP: Algero-Provencal Basin, I:Ionian Basin and L: Levantine Basin).

towards pollution and disturbance) and 5 (persisters:long life cycle, low reproduction potential, sensitiveto disturbance and pollution) (Bongers, 1990). Generawith a c–p score of 2, 3 or 4 are intermediate betweencolonisers and persisters. Monhysterid genera were as-signed to the c–p 2 class (“general opportunists”) as ad-vised by Bongers et al. (1995), and as such there wereno nematodes belonging to c–p class 1 (“enrichment op-portunists”). We calculated the Shannon–Wiener diver-sity index based on the partitioning of nematode indi-viduals over the 4 c–p classes encountered, and termedthisc–p diversity.

– As measures for taxonomic or phylogenetic diversity(not to be confused with the “phylogenetic diversity in-dex” PD, which is an example of a phylogenetic diver-sity index; see Clarke and Warwick, 2001b), we calcu-latedaverage taxonomic distinctnessbased on quantita-tive (1∗) and presence–absence data (1+) (with lowerdistinctness indicative of a higher average relatedness),as well as thevariation in taxonomic distinctness(3+, ameasure for the imbalance of the taxonomic tree, basedon presence–absences) (for formulas see Clarke andGorley, 2006; Clarke and Warwick, 2001a; Warwickand Clarke, 1998). Assuming that3+ indicates func-tional unevenness, and higher values point to less func-tionally diverse communities, we used 1/3+ to quantifytaxonomic or functional evenness. The two average tax-onomic distinctness metrics measure functional diver-gence. Using the ellipse plots in the TAXDEST routine

in Primer, we investigated whether1+ and 3+ weremechanistically related (Clarke and Warwick, 2001a).We used the following taxonomic levels to calculate thephylogenetic or taxonomic diversity indices: class, sub-class, order, suborder, superfamily, family and genus,according to the classification by De Ley et al. (2006),and assumed equal step length.

– Finally, we measured length (L, µm) and width (W ,µm) of all nematodes that were mounted on slidesfor identification purposes to estimate individual wetweight (WW) using Andrassy’s (1956) formula, ad-justed for the specific gravity of marine nematodes(i.e. 1.13 g cm−3; µg WW =L × W2 / 1 500 000). Indi-vidual biomass (B) in terms of µg C ind−1 was then cal-culated as 12.4 % of WW (Jensen, 1984). Next, wecalculatedbiomass diversity(BD) using a Shannon–Wiener diversity expression adapted for continuousvariables according to Quintana et al. (2008). The com-putation was performed in the Diversity08 softwareavailable athttp://limnolam.org/.

Taxon (genus) and phylogenetic diversity indices were cal-culated in Primer v6 (Clarke and Gorley, 2006).

In addition to these diversity indices, we computed thema-turity index(MI) of a nematode assemblage as the weightedaverage of the individual genus c–p values:

MI =

n∑i=1

v (i)f (i),


http://limnolam.org/


Table 1. Sampling details. Indicated are the region where samples were collected in (GB: Galicia Bank region, A: Algerian Basin, AP:Algero-Provencal Basin, I: Ionian Basin and L: Levantine Basin), station code (representing region and approximate water depth), latitude(lat) and longitude (long), range of water depths over replicates, number of replicate samples, surface area of the core, and the research vessel(RV) aboard which samples were taken (SDG:Sarmiento de Gamboa). Lat. and long. are expressed in decimal degrees with negative valuesindicating west (long.) or south (lat.), and positive values indicating east (long.) or north (lat.). Where rounded coordinates of replicatesdiffered, a range is given.

Region Period Station Lat. Long. Depth No. of Core area RV(m) replicates (cm2)

GB Jun/2008 GB1200 42.9 −11.8 1139–1141 3 78.54 BelgicaGB Oct/2008 GB1900 42.4–42.5 −10.7 1770–1896 3 70.88 PelagiaGB Oct/2008 GB3000 41.7 −10.7 3066–3072 3 70.88 PelagiaA Jun/2009 A1200 38.4 1.8 1211–1214 3 69.40SDGA Jun/2009 A1900 38.0 1.9 2004, 2016 2 69.40SDGAP Nov/2009 AP1900 39.4 4.3 1582 3 56.45PelagiaAP Jun/2009 AP3000 38.7 5.5–5.7 2841–2846 3 69.40SDGI Jun/2008 I3000 34.9–35.1 20.5–20.8 2770–2807 7 10.18UraniaL Jun/2008 L1200 35.0 24.6 1026–1143 3 10.18UraniaL Jun/2008 L3000 34.9 24.5 2647 1 10.18Urania

where v(i) = the c–p value of genusi and f (i) the rela-tive abundance of that genus (Bongers, 1990; Bongers et al.,1991, 1995). Hence, the higher the relative abundance of ne-matode genera with a high c–p score, the higher the valueof MI. This functional response measure gives an idea abouthow stable is the environment in which nematodes live.

2.4 Nematode respiration

Individual nematode respiration rates (R; µg C ind−1 d−1)

were calculated on the basis of individual biomass using theformula of Soetaert et al. (1997), which was based on valuesprovided by de Bovee and Labat (1993):

R = 0.0449× B0.8554× explnQ10/10(T −20),

whereQ10 = 2, andT = temperature (◦C; measured at theseabed at each station). Nematode total respiration rates(µg C 10 cm−2 d−1) were computed as the product ofR

with total nematode biomass (µg C 10 cm−2). Total nema-tode biomass (µg C 10 cm−2) was obtained by multiplyingfor each station the arithmetic mean ofB with total density(ind. 10 cm−2).

2.5 Data analysis

Geographic (longitudinal and bathymetric) and environmen-tal trends (relationship with POC, SVI and SED) in nematodediversity, as well as the relationship between diversity andtotal respiration were evaluated with (multiple) linear regres-sion. To account for region-specific bathymetric patterns indiversity, we included an interaction term between depth andlongitude in our models. This interaction term was, however,never significant. Regressions of total respiration against di-versity were run both with (accounting for total biomass)and without total nematode biomass (not accounting for total

biomass) as an independent variable to evaluate confoundingbiomass effects on respiration rates. Relationships amongsttaxon and functional diversity indices were explored withSpearman rank correlations, corrected for multiple testingusing the method of Benjamini and Yekutieli (2001). Here,we used correlation analysis because we did not assume a re-lationship of functional dependence between these variables(Zar, 2010). In addition, we checked for correlations betweentotal abundance and all diversity indices.

For the linear regression analysis, partial residual plotswere used to examine the linearity of the relationship be-tween the dependent and independent variables (Moya-Larano and Corcobado, 2008). The other assumptions oflinear regression (homogeneity of variances, normally dis-tributed residuals, absence of outliers) were checked visuallyon the basis of the residual plots (Zuur et al., 2010). Addition-ally, normality of the residuals and homogeneity of varianceswere tested using a Shapiro–Wilk test and a non-constantvariance score test, respectively. When the variance inflationfactors of the independent variables exceeded 5, indicative ofmulticollinearity, variables were centred (i.e. from each ob-servation the average of the variable was subtracted). Whenassumptions were not met, independent variables or the de-pendent variable were loge transformed or squared. When aunimodal pattern was evident for an independent variable,the quadratic term of this variable was added. The mini-mal adequate model was selected on the basis of theP val-ues of the partial regression tests. Models with and with-out quadratic terms were compared with an ANOVA “lackof fit” test. Our samples were clustered per region (Fig. 1)and thus we checked for spatial autocorrelation which canlead to an increased chance of type I errors (i.e. falsely re-jecting the null hypothesis) (Dormann et al., 2007). We con-ducted global Moran’sI tests on the residuals of all linear



regression models (Plant, 2012), which showed no signifi-cant spatial autocorrelation. Nevertheless, to account for thedependencies between samples collected in the same region,we fitted a linear mixed-effect (LME) model with region as arandom factor and the aforementioned independent variablesas fixed effects to our data. When the likelihood ratio (LR)test indicated that the random region effect was not statis-tically significant (Pinheiro and Bates, 2000), this term wasremoved and we proceeded with the linear regression model(LM). When the random region effect resulted in a signifi-cant improvement of the model (indicating a significant in-fluence of spatial autocorrelation on LM results), however,we interpreted the results of the LME. AdjustedR2 (R2

adj)

and marginalR2 (R2m: variance explained by the fixed ef-

fects; Nakagawa and Schielzeth, 2013) denote the goodnessof fit of the linear models and the linear mixed-effect models,respectively.

All statistical analyses were conducted in R (R Core Team,2012) with the packages car (linear regression assumptionchecks; Fox and Weisberg, 2011), spdep (test for spatial auto-correlation; Bivand, 2012), psych (correlation analysis with acorrection for multiple testing; Revelle, 2012), MuMIn (cal-culation ofR2

m; Barton, 2013) and nlme (fit LME models;Pinheiro et al., 2012). Graphs were made with the ggplot2package (Wickham, 2009). When two independent variableshad a significant effect on nematode diversity or respirationin the LM, the isolated effect of each variable was shown us-ing partial regression plots. We added the means of the rawvariables to the residuals displayed on the axes to place theseon the same scale as the raw variables (Moya-Larano andCorcobado, 2008).

3 Results

3.1 Longitudinal and bathymetric patterns in nematodediversity

The results of the regression analyses examining the longitu-dinal and bathymetric trends in nematode diversity are shownin Table 2. The phylogenetic diversity index1+, the diver-sity of life history strategies (c–p diversity), trophic diversity(TD), the maturity index (MI) and the index of biomass di-versity (BD) showed no trend with water depth or longitude.Shannon–Wiener diversityH ′ (Fig. 2a) and expected genusrichness EG(20) (Fig. 2b) both declined with water depth,but showed no longitudinal trend. Values of Pielou’s even-nessJ ′ (Fig. 2c), taxonomic distinctness based on quantita-tive data1∗ (Fig. 2d) and taxonomic evenness 1/3+ (Fig. 2e)increased from west to east, but remained constant with wa-ter depth. The ellipse plots constructed with the TAXDESTroutine showed that1+ and3+ were not mechanisticallyrelated, meaning they were measuring different properties ofthe taxonomic tree (data not shown).

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Fig. 2. B thymetr c l g tu l tre s em t e vers ty. F r H’ EG 20), partial regression 924 plots were constructed to show the isolated effect of water depth, while the other plots show marginal 925 regress s. H’: Sh -Wiener diversity, EG(20): expected genus richness for a sample of 20 926 v u ls, J’: P el u’s eve ess, Δ*: ver ge t x m c st ct ess se qu t t t ve t , 1/Λ+: 927 taxonomic evenness. The goodness of fit of these regressions is indicated in Table 2. 928

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Fig. 2. Bathymetric and longitudinal trends in nematode diversity.For H ′ and EG(20), partial regression plots were constructed toshow the isolated effect of water depth, while the other plots showmarginal regressions.H ′: Shannon–Wiener diversity, EG(20): ex-pected genus richness for a sample of 20 individuals,J ′: Pielou’sevenness,1∗: average taxonomic distinctness based on quantitativedata, 1/3+: taxonomic evenness. The goodness of fit of these re-gressions is indicated in Table 2.

3.2 Environmental drivers of nematode diversity

Indices J ′, c–p diversity, 1/3+ and BD did not relate toseasonal variability in POC flux (SVI), sediment particlesize diversity (SED) or seafloor POC flux (POC). BothH ′

(Fig. 3a) and EG(20) (Fig. 3b) showed a positive linear re-lationship with loge-transformed POC. TD related inverselywith SED (Fig. 3c), whereas taxonomic distinctness based onpresence–absence (1+, Fig. 3g) and quantitative data (1∗,Fig. 3e) increased with increasing SED. MI (Fig. 3d) and1∗ (Fig. 3h) were influenced negatively by SVI, and1+ de-clined with increasing POC values (Fig. 3f).

3.3 Relationship between nematode taxon andfunctional diversity

Pielou’s evennessJ ′ was the only diversity index thatwas affected significantly by abundance (Spearman rank,r = −0.87,P < 0.001). After correcting for multiple testing,expected genus richness EG(20) correlated positively withc–p diversity (Spearman rank,r = 0.63, P < 0.01; Fig. 4).The other taxon diversity indices did not relate to any of thefunctional diversity measures.



Table 2.Results of linear models (LM), the likelihood ratio (LR) test and the linear mixed-effect models (LME) for the regression of waterdepth and longitude against nematode diversity. For the independent variables depth and longitude (long), the estimated size of the effectand the associatedP value are given. For EG(20) both long and long2 were retained in the model. ForH ′ and BD, long was squared (long2)to comply with regression assumptions.H ′: Shannon–Wiener diversity, EG(20): expected genus richness for a sample of 20 individuals,J ′:Pielou’s evenness, TD: trophic diversity, c–p diversity: diversity of c–p (life history) classes, MI: maturity index,1+: average taxonomicdistinctness based on presence–absence data,1∗: average taxonomic distinctness based on quantitative data, 1/3+: taxonomic evenness,BD: biomass diversity,R2

adj: adjustedR2, R2m: marginalR2, and G.o.f.: goodness of fit.

LM LR test LME

G.o.f. Depth Longitude G.o.f. Depth Longitude

H ′ R2adj = 0.72 −2.8×10−4 long2: −9.4×10−4 LR = 37.09 R2

m = 0.44 −1.8×10−4 long2: −7.3×10−4

P < 0.001 P < 0.001 P < 0.001 P < 0.001 P < 0.001 P = 0.06

J ′ R2adj = 0.79 – 3.6× 10−3 LR = 9.81 R2

m = 0.66 – 3.1× 10−3

P < 0.001 – P < 0.001 P < 0.01 – P = 0.001

EG(20) R2adj = 0.53 −7.4×10−4 long: 3.5× 10−2; LR = 4.34 R2

m = 0.43 −6.7×10−4 long: 3.6× 10−2;

long2: −2.8×10−3 long2: −2.7×10−3

P < 0.001 P < 0.001 long:P = 0.01; P < 0.05 P < 0.001 long:P = 0.25;long2: P < 0.01 long2: P = 0.13

TD R2adj = 0.17 – 8.0× 10−3 LR = 5.60 R2

m = 0.20 – 8.8× 10−3

P = 0.01 – P = 0.01 P < 0.05 – P = 0.15

C–p div R2adj = 0.09 −2.6×10−5 – LR = 9.62 R2

m = 0.008 −7.0×10−6 –

P = 0.06 P = 0.06 – P < 0.01 P = 0.54 –

MI R2adj = 0.09 – 3.2× 10−3 – – – –

P = 0.06 – P = 0.06 – – –

1∗ R2adj = 0.25 – 0.1 LR = 0.81 – – –

P < 0.01 – P < 0.01 P = 0.37 – –

1+ R2adj = 0.08, 6.6 × 10−4 – - – – –

P = 0.07 P = 0.07 – – – –

1/3+ R2adj = 0.12 – 3.3× 10−6 LR = 0.69 – – –

P < 0.05 – P < 0.05 P = 0.41 – –

BD R2adj = 0.07 – long2: −2.7×10−4 – – – –

P = 0.08 – P = 0.08 – – –

3.4 Effect of diversity on respiration rates in nematodecommunities

In the regressions of diversity against total respiration, onlyindicesH ′ and BD had a statistically significant effect (Ta-ble 4). H ′ showed a positive linear relation with loge-transformed total respiration, and squared BD related posi-tively and linearly with total respiration (Fig. 5a and b). Afteraccounting for biomass (by including this variable in the re-gression against respiration), only taxon diversity indicesH ′

and EG(20) had a positive effect on nematode total respira-tion (Table 5, Fig. 5c and d).

4 Discussion

4.1 Longitudinal and bathymetric patterns in nematodediversity

One of the first steps in unravelling the drivers of biodiver-sity constitutes the description of broad-scale geographicalpatterns. Nematode genus diversity, measured as Shannon–Wiener diversity and expected genus richness EG(20), didnot change along the longitudinal axis between the GaliciaBank (GB) region, in the Northeast Atlantic, and the east-ern Mediterranean. In contrast, similar studies based on ne-matode species found a significant decrease in diversity be-tween the Northeast Atlantic and the southern Adriatic Sea(Danovaro et al., 2009a) and alongside the longitudinal axis



935

936

Fig. 3. Environmental drivers of nematode diversity. Plots A, B, E-H show partial regression results, whilst 937 plots C and D show marginal regressions. POC: seafloor particulate organic carbon flux, SED: sediment 938 p rt cle s e vers ty, SVI: se s l v r l ty PO flux, H’: Sh -Wiener diversity, EG(20): 939 expected genus richness for a sample f 20 v u ls, T : tr ph c vers ty, MI: m tur ty ex, Δ+: 940 average taxonomic distinctness based on presence- se ce t , Δ*: ver ge t x m c st ct ess 941 based on quantitative data. The goodness of fit of these regressions is shown in Table 3. 942

943

944

Fig. 3. Environmental drivers of nematode diversity.(A, B, E–H)show partial regression results, whilst(C) and (D) show marginalregressions. POC: seafloor particulate organic carbon flux, SED:sediment particle size diversity, SVI: seasonal variability in POCflux, H ′: Shannon–Wiener diversity, EG(20): expected genus rich-ness for a sample of 20 individuals, TD: trophic diversity, MI: matu-rity index,1+: average taxonomic distinctness based on presence–absence data,1∗: average taxonomic distinctness based on quan-titative data. The goodness of fit of these regressions is shown inTable 3.

in the Mediterranean Basin (Danovaro et al., 2008b, 2009b,2010). Even though genus richness remained relatively con-stant along the longitudinal axis, generic (J ′) and taxonomicevenness (1/3+), as well as the average taxonomic distinct-ness amongst individuals (1∗, quantitative data) increasedtowards the east. Both a higher generic evenness and a greaterdistance between the more abundant genera in the taxonomictree can result in higher values of1∗. Hence, compared tonematode communities in the east, nematode assemblages inthe west were characterised by a more imbalanced taxonomictree (more unequal spread of genera across the taxonomictree) and a more uneven spread of individuals over the dif-ferent genera, whether or not in combination with a lowertaxonomic distinctness (or higher relatedness) between thedominant genera.

We observed a decline in taxon diversity indicesH ′ andEG(20) with increasing water depth, contrasting with nu-merous previous reports of a unimodal diversity–depth trendfor multiple benthic taxa (Menot et al., 2010; Rex and Etter,2010; Stuart et al., 2003). However, the depth range covered

945

946

947

Fig. 4. Significant Spearman rank correlation between taxon diversity (EG(20)) and functional diversity 948 (c-p diversity) of nematodes. The red line and associated grey zone represent a LOESS smoother and the 949 95 % confidence interval, respectively. EG(20): expected genus richness for a sample of 20 individuals, c-p 950 diversity: diversity of c-p (life history) classes. 951

952

953

954

955

956

Fig. 4. Significant Spearman rank correlation between taxon diver-sity (EG(20)) and functional diversity (c–p diversity) of nematodes.The red line and associated grey zone represent a LOESS smootherand the 95 % confidence interval, respectively. EG(20): expectedgenus richness for a sample of 20 individuals, c–p diversity: diver-sity of c–p (life history) classes.

957

958

Fig. 5. Relationship between diversity and total respiration in nematode communities. Plots A and B show 959 the marginal regressions of diversity against respiration (not accounting for total nematode biomass), 960 while plots C and D show the partial regressions of diversity against respiration (accounting for total 961 em t e m ss). H’: Sh -Wiener diversity, BD: biomass diversity, EG(20): expected genus 962 richness for a sample of 20 individuals. 963

964

Fig. 5. Relationship between diversity and total respiration in ne-matode communities.(A) and (B) show the marginal regressionsof diversity against respiration (not accounting for total nematodebiomass), while(C) and(D) show the partial regressions of diver-sity against respiration (accounting for total nematode biomass).H ′: Shannon–Wiener diversity, BD: biomass diversity, EG(20): ex-pected genus richness for a sample of 20 individuals.

here is relatively narrow (1026–3072 m, Table 1) and diver-sity may be depressed at shallower depths. In other words, itis possible that our samples fell within the descending sec-tion of the unimodal bathymetric diversity curve. Danovaroet al. (2010), who considered a larger depth range thanus, discovered a hump-shaped bathymetric trend in nema-tode species diversity, albeit only in the eastern Mediter-ranean Basin. In contrast, Tselepides et al. (2000) described



Table 3.Results of linear models (LM), the likelihood ratio (LR) test and linear mixed-effect models (LME) for the regression of sedimentparticle size diversity (SED), seafloor particulate organic carbon flux (POC) and seasonal variability in POC flux (SVI) against nematodediversity. The estimated size of the effect and the associatedP value are given per independent variable. For the regressions againstH ′, J ′,EG(20) and c–p diversity (c–p div), POC was loge transformed to comply with the assumptions of linear regression. For c–p diversity, alsoSVI was loge transformed to comply with assumptions.H ′: Shannon–Wiener diversity, EG(20): expected genus richness for a sample of 20individuals,J ′: Pielou’s evenness, TD: trophic diversity, c–p diversity: diversity of c–p (life history) classes, MI: maturity index,1+: averagetaxonomic distinctness based on presence–absence data,1∗: average taxonomic distinctness based on quantitative data, 1/3+: taxonomicevenness, BD: biomass diversity,R2


LM LR test LME

G.o.f. SED POC SVI G.o.f. SED POC SVI

H ′ R2adj = 0.70 −0.72 log(POC): 0.68 – LR = 38.81 R2

m = 0.42 −0.27 log(POC): 0.45 –

P < 0.001 P < 0.01 P < 0.001 – P < 0.001 P = 0.07 P < 0.001 –

J ′ R2adj = 0.70 – log(POC):−0.06 −0.21 LR = 17.19 R2

m = 0.29 – log(POC): 0.003 −0.27

P < 0.001 – P < 0.001 P = 0.001 P < 0.001 – P = 0.80 P = 0.09

EG(20) R2adj = 0.47 – log(POC): 1.46 −5.29 LR = 5.65 R2

m = 0.44 – log(POC): 1.50 −4.87

P < 0.001 – P < 0.001 P < 0.001 P < 0.05 – P < 0.001 P = 0.06

TD R2adj = 0.26 −0.68 – - LR = 2.95 – – – –

P < 0.01 P < 0.01 – - P = 0.09 – – –

C–p div R2adj = 0.22 −0.68 log(POC): 0.06 log(SVI):−0.07 LR = 7.15 R2

m = 0.08 – log(POC): 0.02 log(SVI):−0.05

P = 0.01 P < 0.01 P < 0.01 P = 0.01 P < 0.01 – P = 0.39 P = 0.42

MI R2adj = 0.19 – – −0.53 LR = 0.17 – – – –

P < 0.01 – – P < 0.01 P = 0.68 – – –

1∗ R2adj = 0.38 4.20 – −13.90 LR = 020 – – – –

P < 0.001 P < 0.05 – P < 0.001 P = 0.65 – – –

1+ R2adj = 0.23 4.51 −0.25 – LR= 1.12× 10−8 – – –

P = 0.01 P < 0.01 P < 0.05 – P = 0.99 – –

1/3+ R2adj = 0.03 – −1.24× 10−5 – LR = 0.69 – – –

P = 0.17 – P = 0.17 – P = 0.41 – –

BD R2adj = 0.03 0.23 – – – – – –

P = 0.17 P = 0.17 – – – – –

a decrease in macrofaunal diversity between 40 and 1570 mwater depth along the Cretan continental margin. Rex andEtter (2010) speculated that when nutrient loadings becomevery scarce, as is the case in the Mediterranean, there is ashift from a fully unimodal diversity–depth curve towardsjust the ascending portion (i.e. positive association betweenproductivity and diversity). Alternatively, the absence of apeak in diversity at intermediate water depths may be re-lated to the unusually warm (13◦C) and isothermal water col-umn in the Mediterranean (Tyler, 2003). Unimodal diversity–depth trends are generally found in open oceans like the At-lantic and the Pacific where temperature decreases rapidly (tobarely a few degrees) with depth. As opposed to Danovaro etal. (2009a) and Danovaro et al. (2010), bathymetric diversitypatterns did not vary between the different regions that weresampled.

The divergence between the present results and those ofDanovaro et al. (2008b) and Danovaro et al. (2010) regardingbathymetric and longitudinal trends in nematode diversitymay be attributed to the different taxonomic levels (genera

and species, respectively) and sediment depth strata that wereinvestigated (0–10 and 0–1 cm, respectively). For deep-seanematodes inhabiting the Kenyan continental margin, spatialpatterns in genus diversity differed substantially from speciesdiversity trends (Muthumbi et al., 2011). In contrast, Leducet al. (2012b) found very comparable environmental trends inspecies and genus diversity at the continental slope of NewZealand. The difference in species and genus patterns alongthe Kenyan margin (Muthumbi et al., 2011) were attributed tothe unequal distribution of the number of species per genus,with some genera consisting of many species (e.g.Acantho-laimus, De Mesel et al., 2006) and many genera consisting ofa few species.

4.2 Environmental drivers of nematode diversity

Productivity and its mediation of biological interactions hasbeen proposed as a potential mechanism for the commonlyobserved unimodal bathymetric and linear latitudinal diver-sity gradients in deep-sea sediments (Levin et al., 2001; Stu-art et al., 2003). Here, the magnitude of seafloor POC flux



Table 4.Results of linear models (LM), the likelihood ratio (LR) test and linear mixed-effect models (LME) for the regression of nematodediversity against total respiration. The estimated average size of the effect and the associatedP value are given per diversity index. ForH ′

and EG(20), respiration (resp) was loge transformed to comply with the assumptions of linear regression.J ′ and BD were loge transformedand squared, respectively, to comply with assumptions.H ′: Shannon–Wiener diversity, EG(20): expected genus richness for a sample of 20individuals,J ’: Pielou’s evenness, TD: trophic diversity, c–p diversity: diversity of c–p (life history) classes, MI: maturity index,1+: averagetaxonomic distinctness based on presence–absence data,1∗: average taxonomic distinctness based on quantitative data, 1/3+: taxonomicevenness, BD: biomass diversity,R2


LM LR test LME

G.o.f. Effect of diversity G.o.f. Effect of diversity

H ′ R2adj = 0.71 log(resp): 2.45 LR = 4.25 R2

m = 0.51 1.80

P < 0.001 P < 0.001 P < 0.05 P < 0.001

J ′ R2adj = 0.35 log(J ′): −1.35 LR = 6.99 R2

m = 0.02 −0.99

P < 0.001 P < 0.001 P < 0.01 P = 0.09

EG(20) R2adj = 0.24 log(resp):−9.85 LR = 26.43 R2

m = 0.02 Log(resp): 0.17

P < 0.01 P < 0.01 P < 0.001 P = 0.23

TD R2adj = 0.00 −0.10 – – –

P = 0.37 P = 0.37 – –

C–p div R2adj = 0.10 0.88 LR = 11.10 R2

m = 0.0008 0.08

P = 0.05 P = 0.05 P < 0.001 P = 0.87

MI R2adj = 0.00 −0.08 – – –

P = 0.70 P = 0.70 – –

1∗ R2adj = 0.00 0.005 – – –

P = 0.56 P = 0.56 – –

1+ R2adj = 0.02 0.01 – – –

P = 0.23 P = 0.23 – –

1/3+ R2adj = 0.00 −52.08 – – –

P = 0.37 P = 0.37 – –

BD R2adj = 0.40 BD2: 0.14 LR = 8.21 R2

m = 0.18 0.09

P < 0.001 P < 0.001 P < 0.01 P < 0.01

had a positive impact on nematode taxon diversity, measuredas H ′ and EG(20), consistent with earlier work on poly-chaetes (Glover et al., 2002) and nematodes (Lambsheadet al., 2002) from the abyssal central Pacific. It was shownthat seafloor POC flux declines from the Northeast Atlanticto the eastern Mediterranean (not considering seamount sta-tion GB1200) and with water depth (Pape et al., 2013), andhence this environmental factor may partly explain the ob-served bathymetric decline in taxon diversity (see Sect. 4.1).The detection of a positive association between diversity andproductivity does not necessarily negate the existence of ahump-shaped productivity–diversity curve. The productivitygradient considered in this study may occupy only the left,ascending limb of the unimodal diversity–productivity curve(Levin et al., 2001). In support of this, in the Atlantic and theGulf of Mexico, Menot et al. (2010) found a diversity peakat an organic carbon flux of 10–15 g C m−2 yr−1 for several

macrofaunal phyla, which is the maximum value of seafloorPOC flux observed in our study area. As opposed to the tradi-tionally employed diversity measures (i.e. Shannon–Wienerdiversity and expected genus richness), average taxonomicdistinctness (based on presence–absence data,1+) was in-versely related to seafloor POC flux. Hence, along our tran-sect, areas characterised by higher POC input harboured ahigher number of relatively closely related genera, whereasareas receiving less POC were inhabited by less, but moredistantly related genera. It seems that a high POC flux regimeis favouring a higher number of nematode genera that are rel-atively closely related and consequently exhibit similar prop-erties that allow them to outcompete other genera or with-stand predation pressure by larger fauna.

In the present study, higher seasonality in surface produc-tivity (SVI) was reflected in a reduced nematode maturity in-dex, governed by the increased contribution of colonisers or



Table 5.Results of linear models (LM), and the likelihood ratio (LR) test for the regression of nematode diversity and total nematode biomassagainst total respiration. Since the LR test was never significant, only the LM results were interpreted. The estimated average size of the effectand the associatedP value are given for biomass and each diversity index. Except for the regressions withH ′ and EG(20), biomass (bio)was loge transformed to comply with the assumptions of linear regression.H ′: Shannon–Wiener diversity, EG(20): expected genus richnessfor a sample of 20 individuals,J ′: Pielou’s evenness, TD: trophic diversity, c–p diversity: diversity of c–p (life history) classes, MI: maturityindex,1+: average taxonomic distinctness based on presence–absence data,1∗: average taxonomic distinctness based on quantitative data,1/3+: taxonomic evenness, BD: biomass diversity,R2


LM LR test

G.o.f. Effect of biomass Effect of diversity

H ′ R2adj = 0.66 0.02 0.15 LR = 3.56

P < 0.001 P < 0.001 P < 0.01 P = 0.06

J ′ R2adj = 0.69 log(bio): 0.12 0.57 –

P < 0.001 P < 0.001 P = 0.23 –

EG(20) R2adj = 0.63 0.02 0.05 LR = 2.85

P < 0.001 P < 0.001 P = 0.01 P = 0.09

TD R2adj = 0.68 log(bio): 0.10 −0.03 –

P < 0.001 P < 0.001 P = 0.65 –

C–p div R2adj = 0.68 log(bio): 0.10 −0.007 –

P < 0.001 P < 0.001 P = 0.98 –

MI R2adj = 0.67 log(bio): 0.10 −18.3×10−4 –

P < 0.001 P < 0.001 P = 0.99 –

1∗ R2adj = 0.68 log(bio): 0.10 −0.003 –

P < 0.001 P < 0.001 P = 0.50 –

1+ R2adj = 0.68 log(bio): 0.10 −0.005 –

P < 0.001 P < 0.001 P = 0.45 –

1/3+ R2adj = 0.68 log(bio): 0.10 13.12 –

P < 0.001 P < 0.001 P = 0.70 –

BD R2adj = 0.68 log(bio): 0.09 0.09 –

P < 0.001 P < 0.001 P = 0.36 –

opportunists to nematode standing stock (Bongers and Ferris,1999; Bongers et al., 1991). It is believed that these nema-todes can cope better with variable environmental conditionssuch as those induced by pulsed organic matter input. Nema-tode communities in more seasonal regions displayed alsolower average taxonomic distinctness (based on quantitativedata,1∗). This finding suggests that the ability to maintainhigh abundances under a more pulsed organic matter loadingmay be confined to certain taxonomic groups. Clearly, ourresults imply that both the magnitude and the seasonality ofseafloor POC flux impact the average taxonomic distinctnesswithin nematode communities, which may be translated toa greater functional distinctness. However, since taxonomicdistinctness may be governed by a variety of factors, suchas biogeography, environmental factors, habitat characteris-tics, and stress (Bevilacqua et al., 2012; Leira et al., 2009;Mouillot et al., 2005; Warwick and Clarke, 1995, 1998; Xu

et al., 2011), more research into life history strategies, nicherequirements and taxon interactions are needed to fully un-derstand the patterns observed here.

Unlike Leduc et al. (2011) (nematode species and genera)and Etter and Grassle (1992) (macrofaunal species), we didnot detect an effect of sediment heterogeneity (SED) on ne-matode genus diversity. We did, however, observe that moreheterogeneous sediments harboured nematode assemblageswith a higher taxonomic breadth, and possibly a higherfunctional divergence. The higher habitat heterogeneity mayfavour the co-existence of more taxonomically dissimilartaxa, with their distinct specific niche requirements. Possibly,the high genus diversity observed by Leduc et al. (2011) co-incided with high taxonomic distinctness. Leduc et al. (2011)found no effect of SED on nematode trophic diversity (TD),whereas we uncovered an inverse relationship between SEDand TD. It should be stressed that the trends described here



do not imply causal relationships, and that the decrease innematode trophic diversity with increasing SED may bedriven by a confounding, unmeasured environmental factor(e.g. standing stock of mega or macrofauna). The differen-tial calculation of sediment heterogeneity hampers the com-parison between our study and that of Leduc et al. (2011).Whereas we considered ten different grain size classes (seeSect. 2.2), Leduc et al. (2011) used only five sediment grainsize classes in their calculation of SED without subdividingthe mud fraction (< 63 µm) of the sediment. Finally, note thatour SED calculation and that of Etter and Grassle (1992) andLeduc et al. (2011) was based on dry-sieved sediment frac-tions and it is possible that this measure of particle diversityis not representative for the in situ size distribution of aggre-gated sediment particles (Levin et al., 2001; Snelgrove andButman, 1994).

4.3 Link between nematode taxon and functionaldiversity

Our results showed that nematode communities with highertaxon diversity were characterised by a greater variety oflife history strategies (higher c–p diversity). If higher c–pdiversity governs enhanced resistance against environmen-tal fluctuations or resilience following disturbance, this maypoint to a positive long-term effect of taxon diversity onecosystem functioning (Loreau, 2000). We found no linksbetween the other taxon and functional diversity measures,and hence the presence of a relationship between taxon andfunctional diversity depended on the type of functional traitsconsidered. However, the functional diversity indices com-puted here might not encompass the entire array of functionsperformed by the nematode community. For instance, thefeeding type classification scheme based on buccal morphol-ogy (Wieser, 1953) may be too coarse to represent a truth-ful proxy for resource partitioning. In support, De Mesel etal. (2003) observed that coastal nematode species belongingto the same feeding guild had a differential influence on cord-grass decomposition rates. The validity of our results con-cerning the association between taxon and functional diver-sity in other oceanographic regions remains to be tested as itis partly determined by the degree of redundancy and singu-larity within a community, as well as by biogeography andbiotic interactions (Hooper et al., 2002; Naeem and Wright,2003).

4.4 Effect of diversity on respiration rates in nematodecommunities

The present study showed that deep-sea nematode commu-nities with higher Shannon–Wiener genus diversity (H ′) orhigher individual biomass diversity (BD) showed higher to-tal respiration rates, and that the influence of both diversityindices was more pronounced at higher values (as inferredfrom the exponential and power function describing the de-

pendency of respiration onH ′ and BD, respectively). How-ever, when differences in total nematode biomass betweensamples were accounted for, we only observed a positivelinear relationship between taxon diversity (measured asH ′

and expected genus richness EG(20)) and total respiration. Inother words, nematode communities with the same standingstock showed different respiration rates when genus diver-sity, but not biomass diversity, differed. Hence, the positiveimpact of BD on respiration could be attributed to the pos-itive covariance between total biomass and the diversity inindividual biomass.

More diverse nematode assemblages may mineralise morecarbon when the co-existence of more genera results in amore complete utilisation of all different carbon sources.Consequently, the linear form of the relation between ex-pected genus richness and total respiration may indicate thatall genera contributed more or less equally to the decompo-sition and mineralisation of organic matter, which is in linewith the rivet hypothesis (Lawton, 1994; Naeem et al., 1995).Contrary to expectations, we found no proof for functionaldiversity as a mechanistic link between taxon diversity andecosystem functioning since none of the functional diversityindices (including taxonomic or phylogenetic diversity) re-lated to both genus diversity and total respiration. Severalpoints can be raised to explain the lack of a significant associ-ation between the functional diversity indices and ecosystemfunctioning. First of all, as mentioned in section 4.3, the met-rics computed here may not adequately represent true func-tional diversity. The diversity in diet composition amongstnematode genera may not be captured by the trophic diver-sity index. Secondly, the functional diversity measures usedhere are perhaps not important for the ecosystem functionunder study, but they may well be for other functions per-formed by nematodes. For instance, a nematode communitywhich comprises a wide variety of differently sized individ-uals (high BD) may create more diversified micro-burrownetworks within the sediment. This type of cryptobioturba-tion and bioirrigation may in turn stimulate small-scale yetimportant biogeochemical processes (Aller and Aller, 1992;Pike et al., 2001), resulting in elevated carbon mineralisationby the entire benthic community. A more relevant measure offunctional diversity to nematode carbon mineralisation maybe the diversity in digestive systems, mirrored in taxon diver-sity. Thirdly, it is possible that environmental conditions in-fluencing both functional diversity and respiration rates varyamong sites, resulting in an absence of an across-site patterneven when significant biodiversity effects exist within eachlocale (Cardinale et al., 2000; Hiddink et al., 2009; Loreau,2000). A fourth point is that total nematode respiration washere estimated on the basis of total nematode biomass andtemperature (de Bovee and Labat, 1993) and may not truth-fully reflect in situ respiration by the nematode community.Environmental factors other than temperature (Braeckman etal., 2013) and biotic interactions (De Mesel et al., 2006) mayinfluence nematode carbon processing rates. Sounder results



would have been obtained from the direct measurement ofoxygen consumption or carbon dioxide production by nema-tode communities in deep-sea sediments.

Phylogenetic diversity may serve as a proxy for functionaldiversity when related taxa are functionally similar (Cadotteet al., 2008, 2009). The present study did not demonstrate asignificant effect of phylogenetic diversity on total respira-tion for deep-sea nematodes. The lack of a relation betweenphylogenetic diversity and ecosystem functioning may be be-cause (1) functionally important traits do not have a strongphylogenetic signal (or in other words, closely related taxado not have similar functional trait values), (2) the signal isreduced because of community assembly, or (3) traits thatdetermine dominance are not important for the function un-der study (Srivastava et al., 2012). The fact that we found nolink between taxon and phylogenetic diversity, whereas totalrespiration was affected positively by taxon diversity, impliesthat the phylogenetic diversity indices used here do not serveas good surrogates for the diversity of traits important for to-tal respiration (such as feeding behaviour and/or the digestiveapparatus).

The different form of the biodiversity–function curve inthe present study (linear) and that of Danovaro et al. (2008a)(exponential) may be related to the differences in measures ofecosystem function (nematode respiration rates vs prokary-ote biomass and production, bacterial organic matter decom-position and total faunal biomass, respectively), the taxo-nomic level considered (genera vs. species, respectively), andthe sediment depth interval investigated (0–10 cm vs 0–1 cm,respectively). As demonstrated for shallow marine and ter-restrial systems, different ecosystem processes or propertiescan respond very dissimilarly to changes in biodiversity (Bo-lam et al., 2002; Naeem et al., 1995).

We assumed that respiration is dependent on diversity innematode communities. However, significant regressions donot necessarily imply causation. If both diversity and respi-ration are influenced by the same environmental factor(s),this would also result in a significant relationship. Temper-ature promotes respiration rates and, at least for ophiuroids(O’Hara and Tittensor, 2010) and mollusks (Tittensor et al.,2011), also biodiversity. Along our transect, higher POC de-position (food availability) resulted in higher standing stock(Pape et al., 2013) and thus higher respiration rates, but alsoallowed for more taxa to attain viable population sizes. How-ever, the fact that communities with equal biomass (suggest-ing equal food availability) with differing diversity showeddiffering respiration rates indicates that food availabilityis not the sole factor governing the significant diversity–function relation. Experimental studies, employing in siturespiration as an ecosystem function, are needed to verify andelucidate the mechanism(s) behind the observed diversity–function relation.

Since climate change is already affecting the pattern ofPOC flux to the deep-sea bottom and will continue to do so(Smith et al., 2008), the links between the magnitude andvariability in POC flux and taxon diversity, on the one hand,and between taxon diversity and ecosystem functioning, onthe other hand, suggests that this global phenomenon willmodify, or already is modifying, the functioning of deep-seaecosystems.

5 Conclusions

Several of the nematode diversity indices that we calculateddisplayed significant bathymetric and longitudinal patterns,which may be partly ascribed to variations in the rate andseasonality of organic matter deposition as well as in sedi-ment heterogeneity. Accounting for confounding biomass ef-fects, we observed a positive linear relationship between ne-matode taxon diversity and nematode carbon mineralisation,estimated from total nematode biomass. The fact that noneof the indices of functional diversity, including phylogeneticdiversity, related to both taxon diversity and mineralisationrates suggests that these indices did not encompass the entirearray of nematode functional traits that are of importance tonematode carbon mineralisation. Our results suggest poten-tial effects of climate change on nematode carbon minerali-sation rates in the deep sea. In light of the progressive changein global climatic patterns, it is clear that we urgently need toimprove our knowledge regarding the functions that nema-todes perform within deep-sea sedimentary ecosystems andhow these are affected by evolutionary processes.

Acknowledgements.This research received funding throughthe ESF-EuroDEEP BIOFUN project (FWO project number3G067007) and the European Commission’s Seventh FrameworkProgramme HERMIONE project (grant number 226354), aswell as through the FWO project G083512W. We are indebtedto the crew and scientific personnel during expeditions aboardthe RV Belgica (Belgica cruise 2008/13b), RVPelagia (cruises64PE295-296 and 64PE314), RVUrania (BIOFUN cruise 2008)and Sarmiento de Gamboa(BIOFUN 2009 Trans-Mediterraneancruise). Bart Beuselinck and Niels Viaene are thanked for grain sizeanalyses. We appreciated the help of Niels Viaene in measuringnematode biovolume and extracting meiofauna, and that of AnnickVan Kenhove and Guy De Smet in preparing slides for nematodeidentification. Last but not least, the authors greatly appreciated thestatistical advice provided by Professor Carl Van Gestel, and thehelp with the interpretation of the taxonomic distinctness indicesby Professor Bob Clarke.

Edited by: R. Danovaro



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